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Data Quality
Assure reliability, consistency, and accuracy data from different sources to deliver value to employees and customers.
Data Quality pipelines
Pipelines define and apply transforms to data loaded by a dataset.
Reference
This section contains reference information for pipeline pages in Precisely DQ.
Data Quality Transforms
Pipeline transform settings are configured to add transformation steps in a pipeline. This reference describes the purpose of each transform and its settings that define a transformation step in a pipeline.
Replace Between
Replaces a substring between two specified characters.

Welcome
The Precisely Data Integrity Suite is designed to deliver trusted data with accuracy, consistency, and context – built on the core capabilities of data integration, data quality, data governance, location intelligence, and data enrichment.
Data Integration
Replicate data from mainframe, IBM i, and relational databases to cloud analytics platforms.
Data Observability
Proactively monitor and manage the health of your data. Receive actionable insights through alerts that drive business decisions.
Data Governance
Manage data policy and processes with greater insight into your data's meaning, lineage, and impact.
Data Quality
Assure reliability, consistency, and accuracy data from different sources to deliver value to employees and customers.
- Data Quality Catalogs
  The Data Integrity Suite Data Catalog lets users discover and understand connected data sources.
- Data Quality datasets
  A dataset defines a data source and characterizes data to be loaded and transformed by pipelines.
- Data Quality pipelines
  Pipelines define and apply transforms to data loaded by a dataset.
  - About Data Quality pipelines
  - Using Data Quality pipelines
  - How to
  - Reference
    This section contains reference information for pipeline pages in Precisely DQ.
    - Pipelines
      The table on this page lists pipelines. On this page you can create a new pipeline or edit, delete, or rename any existing pipeline.
    - Pipeline settings
      The pipeline settings page allows you to view and edit transformation steps in a pipeline.
    - Add Step
      Choose a transformation in this dialog box to add it as a step in a pipeline.
    - Data Quality Transforms
      Pipeline transform settings are configured to add transformation steps in a pipeline. This reference describes the purpose of each transform and its settings that define a transformation step in a pipeline.
      - Case Field
        Converts strings in a field into upper, lower, or title case.
      - Copy Column
        Copies a column to one or more new columns.
      - Execute Formula
        Performs operations on values and inserts the result in a new column.
      - Filter Column
        Removes selected columns from a table.
      - Get Substring
        Returns the substring from the start position to the end position. Index of the first character is 1.
      - Pad String
        Pads the beginning or end of field content with a string to a specified length. The Pad field transform does not change the value of the original string.
      - Parse Name
        This transformation parses personal and business names into their constituent parts, such as given name, surname, and titles and qualifiers.
      - Rename Column
        Renames a column in a pipeline.
      - Replace Between
        Replaces a substring between two specified characters.
      - Replace By Position
        Replaces the substring from the start position to the end position.
      - Replace Consecutive Spaces
        Replaces all instances of two or more consecutive spaces in a string field value with a single space.
      - Filter Row
        The row filter evaluates whether to include rows depending on a logical expression that evaluates to either true or false.
      - Search and Replace
        Use Search and Replace in a pipeline to search for strings in columns and replace them with a different string.
      - Split Column
        Splits a column into two new columns.
      - Standardize Date
        If a data column contains dates in different formats, use the Standardize Date transformation to convert values in all records to a single format.
      - Standardize Field
        The Standardize Field transformation applies one or more standard conversions to semantically typed fields.
      - Trim String
        Removes specified characters from the start or end of a string.
      - Verify Address
        The Verify Address transform parses, matches, and formats address data against reference postal data. A confidence level is assigned to each verified record.
- Data Quality jobs
- Data Connections
  Connecting to data is the first step in processing, analyzing, and storing information.
Administration
Manage your subscription, your users and their roles, and other system tasks.

Replace Between

Replaces a substring between two specified characters.

Step name: Defines the name for a step. Provide a meaningful name so that anyone who edits transformation steps in a pipeline will be able to identify the purpose of a step.
Columns: Specifies columns to transform. Click to view the list of column names. Column names listed here correspond to the column names in the dataset inspection table. Click in the drop-down list to select or clear check boxes next to column names. Alternatively, click column headings in the inspection table to select or clear check boxes next to the corresponding column names in this box. To add a column to the selection, you must press the the Ctrl key when you click a column heading.
Start character: Start character in the string. The transform will replace characters in the substring that follow this character.
End character: End character in the string. The transform will replace characters that the substring precede this character.
Replacement: Specifies the string to replace the substring between the start and end characters.
Save: Click this button to close settings and save changes to the transformation settings.
Preview: Click this button to preview the results of the transformation settings.
Cancel: Click this button to close settings for this transformations without saving any changes.

Example

Replace substring between start character "-" and end character "-" with "month" in 2021-12-08.

Result: 2021-month-08